DEBISS: a Corpus of Individual, Semi-structured and Spoken Debates
This corpus addresses the scarcity of diverse debate corpora, which is a problem for researchers developing NLP models for debate analysis.
The authors created DEBISS, a new corpus of individual, semi-structured, and spoken debates. This corpus includes annotations for various NLP tasks such as speech-to-text, speaker diarization, argument mining, and debater quality assessment.
The process of debating is essential in our daily lives, whether in studying, work activities, simple everyday discussions, political debates on TV, or online discussions on social networks. The range of uses for debates is broad. Due to the diverse applications, structures, and formats of debates, developing corpora that account for these variations can be challenging, and the scarcity of debate corpora in the state of the art is notable. For this reason, the current research proposes the DEBISS corpus: a collection of spoken and individual debates with semi-structured features. With a broad range of NLP task annotations, such as speech-to-text, speaker diarization, argument mining, and debater quality assessment.